The Delhi government is collaborating with IIT Kanpur to build an AI-enabled decision support system (DSS) targeting the capital’s chronic air pollution through hyperlocal source identification and real-time interventions. The platform will integrate sensor networks, advanced analytics, and dynamic source apportionment to pinpoint contributions from road dust, vehicles, industry, biomass burning, and regional factors, replacing blanket restrictions with precise, evidence-based actions.
Hyperlocal Monitoring Replaces Broad Measures
Environment Minister Manjinder Singh Sirsa described the shift under Chief Minister Rekha Gupta as moving from seasonal emergency responses to year-round, data-driven frameworks integrating technology, governance, and enforcement. The system emphasises hotspot-specific enforcement over city-wide bans, using granular pollutant tracking to guide targeted clean-up and emission controls.
Real-time analytics will enable authorities to prioritise interventions based on measurable source impacts rather than averaged air quality indices.
Dynamic Source Apportionment Drives Precision
Unlike current systems relying on outdated emission inventories, the IIT Kanpur DSS promises granular breakdowns of pollution contributions across vehicle exhaust, construction dust, industrial stacks, crop residue burning, and transboundary flows. Sensor fusion and machine learning will process live data streams to generate actionable intelligence for enforcement teams.
The approach addresses criticisms of existing tools like the 2018 Air Quality Early Warning System, which achieves over 80% accuracy in pollution forecasts but underpredicts peak pollutant levels.
Year-Round Framework Challenges Seasonal Focus
Sirsa stressed that pollution control demands 365-day execution rather than winter-only measures, with the new DSS providing continuous monitoring and adaptive strategies. The collaboration builds on IIT Kanpur’s expertise in environmental modelling while addressing gaps in the Indian Institute of Tropical Meteorology’s current decision support.
By focusing on outcomes over optics, Delhi aims to establish a scalable model for other polluted urban centres facing similar multi-source air quality challenges.
